A quality control procedure for Fengyun-3A microwave temperature sounder with emphasis on a new cloud detection algorithm

Abstract A quality control (QC) procedure for satellite radiance assimilation is proposed and applied to radiance observations from the Microwave Temperature Sounder (MWTS) on board the first satellite of the Chinese polar-orbiting Fengyun-3 series (FY-3A). A cloud detection algorithm is incorporated based on the cloud fraction product provided by the Visible and Infrared Radiometer (VIRR) on board FY-3A. Analysis of the test results conducted in July 2011 indicates that most clouds are identifiable by applying an FY-3A VIRR cloud fraction threshold of 37%. This result is verified with the cloud liquid water path data from the Meteorological Operational Satellite A (MetOp-A). On average, 56.1% of the global MWTS data are identified as cloudy by the VIRR-based cloud detection method. Other QC steps include the following: (i) two outmost field of views (FOVs), (ii) use of channel 3 if the terrain altitude is greater than 500 m, (iii) channel 2 over sea ice and land, (iv) coastal FOVs, and (v) outliers with large differences between model simulations and observations. About 82%, 74%, and 29% of the MWTS observations are removed by the proposed QC for channels 2–4, respectively. An approximate 0.5-K scan bias improvement is achieved with QC, with a large impact at edges of the field of regard for channels 2–4. After QC, FY-3A MWTS global data more closely resemble the National Centers for Environmental Prediction (NCEP) forecast data, the global biases and standard deviations are reduced significantly, and the frequency distribution of the differences between observations and model simulations become more Gaussian.

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A quality control procedure for Fengyun-3A microwave temperature sounder with emphasis on a new cloud detection algorithm

Journal of Southern Hemisphere Earth System Science ◽

10.1071/es16004 ◽

2016 ◽

Vol 66 (1) ◽

pp. 19

Author(s):

Xiang Wang ◽

Yifang Ren ◽

Gang Li

Keyword(s):

Quality Control ◽

Liquid Water ◽

Detection Algorithm ◽

Radiative Transfer Model ◽

Transfer Model ◽

Control Procedure ◽

Cloud Detection ◽

Liquid Water Path ◽

Model Simulations ◽

Water Path

A cloud detection algorithm for satellite radiance from the microwave temperature sounder (MWTS) on board the first satellite of the Chinese polar-orbiting Fengyun-three series (FY-3A) is proposed based on the measurements at the frequencies of 50.3 and 53.6 GHz. The cloud liquid water path index (LWP index) is calculated using the brightness temperature at these two channels. Analysis of one case carried out in January2010shows the great consistency be-tween this new algorithm result and the available liquid water path product from the Meteorological Operational satellite A (MetOp-A).In general, about 60% of the global MWTS data are considered to be contaminated by cloud by virtue of the new cloud detection algorithm. A quality control (QC) procedure is applied to MWTS measurements with emphasis on the cloud detection. The QC steps are composed of (i) channel 2 over sea ice, land and coastal field of views (FOVs); (ii) channels 2 and 3 over cloudy FOVs; and (iii) outliers with large differences between observations and model simulations. After QC, MWTS measurements of channels 2–4 agree very well with the model simulations using the National Centres for Environmental Prediction (NCEP) forecast data as radiative transfer model input; the scan biases are reduced significantly, especially at the edges of the swath; and the frequency distributions of the differences between observations and model simulations become more Gaussian-like.

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Expanding HadISD: quality-controlled, sub-daily station data from 1931

10.5194/gi-2016-9 ◽

2016 ◽

Author(s):

Robert J. H. Dunn ◽

Kate M. Willett ◽

David E. Parker ◽

Lorna Mitchell

Keyword(s):

Quality Control ◽

Wind Speed ◽

Time Range ◽

Underlying Structure ◽

Control Procedure ◽

Future Projections ◽

Quality Control Procedure ◽

Temporal Coverage ◽

Extremes Of Temperature ◽

Selection Of

Abstract. HadISD is a sub-daily, station-based, quality-controlled dataset designed to study past extremes of temperature, pressure and humidity and allow comparisons to future projections. Herein we describe the first major update to the HadISD dataset. The temporal coverage of the dataset has been extended to 1931 to present, doubling the time range over which data are provided. Improvements made to the station selection and merging procedures result in 7677 stations being provided in version 2.0.0.2015p of this dataset. The selection of stations to merge together making composites has also been improved and made more robust. The underlying structure of the quality control procedure is the same as for HadISD.1.0.x, but a number of improvements have been implemented in individual tests. Also, more detailed quality control tests for wind speed and direction have been added. The data will be made available as netCDF files at www.metoffice.gov.uk/hadobs/hadisd and updated annually.

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Migration from NetWISP to Nutritics – a quality control procedure

Proceedings of The Nutrition Society ◽

10.1017/s0029665118000939 ◽

2018 ◽

Vol 77 (OCE3) ◽

Author(s):

S. Cassidy ◽

B. Phillips ◽

J. Caldeira Fernandes da Silva ◽

A. Parle

Keyword(s):

Quality Control ◽

Control Procedure ◽

Quality Control Procedure

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A nonparametric on-line quality control procedure for vectorial observations

Journal of Nonparametric Statistics ◽

10.1080/10485252.2012.740039 ◽

2013 ◽

Vol 25 (1) ◽

pp. 21-32

Author(s):

Jun Mao ◽

David R. McDonald ◽

Mahmoud Zarepour

Keyword(s):

Quality Control ◽

Control Procedure ◽

Quality Control Procedure ◽

On Line

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Automated Quality Control of In Situ Soil Moisture from the North American Soil Moisture Database Using NLDAS-2 Products

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0275.1 ◽

2015 ◽

Vol 54 (6) ◽

pp. 1267-1282 ◽

Cited By ~ 18

Author(s):

Youlong Xia ◽

Trent W. Ford ◽

Yihua Wu ◽

Steven M. Quiring ◽

Michael B. Ek

Keyword(s):

Quality Control ◽

Soil Moisture ◽

North American ◽

Soil Layer ◽

Control Procedure ◽

Multiple Model ◽

Quality Control Procedure ◽

The North ◽

Automated Quality Control

AbstractThe North American Soil Moisture Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models, and validating satellite-derived soil moisture algorithms. The NASMD has collected data from over 30 soil moisture observation networks providing millions of in situ soil moisture observations in all 50 states, as well as Canada and Mexico. It is recognized that the quality of measured soil moisture in NASMD is highly variable because of the diversity of climatological conditions, land cover, soil texture, and topographies of the stations, and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy, and imprecision in the data can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure that the data are of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System, phase 2 (NLDAS-2), Noah soil porosity, soil temperature, and fraction of liquid and total soil moisture to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the soil is partially frozen. A validation example using NLDAS-2 multiple model soil moisture products at the 20-cm soil layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and west Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1200 NASMD stations in the near future.

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